{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test score: 0.45682283285573283\n",
      "Test accuracy: 0.8157835006713867\n"
     ]
    },
    {
     "data": {
      "image/png": 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ssdZus9Y2Ao8B54f4/mcAr1prK7xC/ypwZs+aGgLRSfLodfGMzUpg077qAUnb9Lfu1aevKMpgIRTRzwWK/V6XeLcFcpExZrUx5iljzPAjOdcYc40xZrkxZnlpaWmITQ9CoOhnJ1Lb2ELJwbqev2cPaVT3jqIog5BQRN8E2RZoOj8HFFprpwCLgH8dwblYa++x1s6w1s7IyMgIoUmdEJMsj22iL8HcgfDrq3tHUZTBSCiiXwIM93udB+zxP8BaW26tdXIj7wWmh3purxLdXvTHZCUADIhfv0FFX1GUQUgoor8MGG2MKTLGRAILgIX+Bxhjcvxenges9z5/GTjdGJNijEkBTvdu6xva3DsyKzchOoLc5JiBt/R1nVxFUQYJ3WbvWGubjTHXI2LtBu631q41xvwcWG6tXQh80xhzHtAMVABXec+tMMb8Auk4AH5ura3og88hBPj0AcZmJ7BxQCx9LcOgKMrgo1vRB7DWvgC8ELDtx37PbwFu6eTc+4H7j6KNoRNg6YOI/lubS2lqaSXC3X9z0RyhdxkVfUVRBg/hNSPXEwWemHaW/rjsBJpaLFsO1PRrUxyhT4iO0OwdRVEGDeEl+tCu/g7AtOEpAKzY2TfzwTrD8enHR3k0T19RlEFDeIp+nc+9Mzw1hvT4qP4X/ZZWItyG6AiXuncURRk0hKfo+1n6xhhmFKT0u+g3NLUS5XET5XGre0dRlEFD+Im+35KJDtMLUthVcZgD1fX91ozGlhYiPS6i1NJXFGUQEX6iH2DpA0wv9Pr1d/SftS+Wvosoj4q+oiiDhyEh+pOGJRHpcfWri6expVUsfY9bRV9RlEFD+Iq+X2XNSI+LY/KSWN6fot/cSqTba+nrjFxFUQYJ4Sn6tgUa2+flTy9IZe2eyn5bM7ehuZWoCBdREe52JRkURVEGkjAU/fZF1xwuOjYXYwzfeXIVra19X1+/naWvoq8oyiAhDEW/Y/0dgNFZCfzwnPEs2VjKA+/u6PNmNDS3eFM2XZqyqSjKoGHIiD7AlXMKOHV8Jre+tIHq+qY+bUZjs18gV2fkKooySBhSom+M4SsnjKChuZU3Nh3FCl0h0NDsTdnUPH1FUQYR4Sv6fqUY/JlekEJqXCSvrtvfp83wWfouGlta+yWOoCiK0h3hJ/oxMhErmKUP4HG7OGVcJq9vOEBTS99Z4A1+7h2QvH1FUZSBJvxEPypRHjsRfYDTJmRRXd/M+9v6bj0Xce9IIBdQv76iKIOC8BN9twci47sU/RNGZxAd4eLVdfv6rBmNzS1tPn1AM3gURRkUhJ/oQ9BSDP7ERLo5YXQGL67Z12di3BbI9bp3NJirKMpgIExFP7ndkonBuGJOAQeqG3hqRUmvX95a61d7Ry19RVEGD2Eq+l1b+gAnjk5nWn4ydy7e2utlEppbLdbSNiMXoF59+oqiDALCWPS7tvSNMdxwymh2H6rj6Q9719p3XDlO7R3/bYqiKANJGIt+15Y+wKfGZDB1eDJ/fW0z9b1YCdMZOfhb+ureURRlMDCkRd8Yw81njWNPZT33vbWt1y7vCHxUhF/Kplr6iqIMAsJT9GOSob4KWrsX2jkj0jhjYhZ3Ltnaa8sptrf0ve4d9ekrijIICE/Rj04CLDRUhXT4LWeNp6mlld+/tLFXLt8m+pqnryjKICOMRZ+QXDwAhelxfHneCJ5cUcLSbeVHffm2QG67lE219BVFGXhU9L3ccMpo8lNj+f5/Pj7qoG6Dv6Wvk7MURRlEqOh7iYl086sLJrGtrJY/vHx0bp62QK7H7XPv6Dq5iqIMAsJU9J0lE7vO1Q/khNEZXDmngPve3s5DS3f2+PLtfPrq3lEUZRDhGegG9Ak9sPQdfnLuBPYcquPHz64hNzmGk8ZlHvF7+Pv0I90q+oqiDB7C1NLvueh73C7++rlpjMlK4AfPfExd45G7ZRr9RN8Yo+vkKooyaAhP0Y9KBEyPRB8gNtLDz86byJ7Keu5+c+sRn+/v3gERf83TVxRlMBCeou9yifD3UPQBZo9I45wpOdz1xla2ltYc0bk+945k7kRFuNW9oyjKoCA8RR8gJrRSDF1xy1njcBvD6X9+k68/8iHFFYdDOq/R68ppZ+mre0dRlEFA+Ip+dFKni6OHSl5KLC/fdCJfnlfEGxtL+fRf32bxxgPdnucfyAUR/94u36woitITQhJ9Y8yZxpiNxpgtxpibuzjuYmOMNcbM8L6OMMb8yxjzsTFmvTHmlt5qeLdEJx+1pQ8i/N8/ezz/++Y8hiXH8KV/LuO2RZtobbWdntPRp+/uUUBYURSlt+lW9I0xbuBvwFnABOAyY8yEIMclAN8E3vfb/Fkgylo7GZgOXGuMKTz6ZodAiJU2Q6UgLY7/fO04Lpiay22LNvPlfy2jur4p6LGNLa0YAx6XAaAwLZZNB6p7rS2Koig9JRRLfxawxVq7zVrbCDwGnB/kuF8AtwL+pSotEGeM8QAxQCMQWhW0o6WXLH1/YiLd/PGSY/j5+RN5c3MZl927lLKaBqrrmyitbmg7zlkf1xgR/ekFKRRX1HGgqneqeCqKovSUUEQ/Fyj2e13i3daGMWYaMNxa+3zAuU8BtcBeYBfwB2ttReAFjDHXGGOWG2OWl5aWHkn7O6eXLX0HYwyfn1vIvZ+fzpYDNcz//RKm/OwV5vzmNR5ftgsQ944zKQtE9AFW7DzY6+1RFEU5EkKZkWuCbGtzaBtjXMCfgauCHDcLaAGGASnAW8aYRdbadiuWWGvvAe4BmDFjRufO8iMhOgkaq6GlGdy9P/H45HFZPHz1HP793g6K0uNYsfMg33v6Y9bsrmLT/uq2ZRIBJg5LIsrjYvnOg5w1OafX26IoihIqoahhCTDc73UesMfvdQIwCVjidWdkAwuNMecBnwNestY2AQeMMe8AM4DeW6aqM5xZuQ1VEJvaJ5eYXpDSZsU3tbTy42fX8KC3Zs/YrIS24yI9Lo4ZnsxytfQVRRlgQnHvLANGG2OKjDGRwAJgobPTWltprU231hZaawuBpcB51trliEvnZCPEAXOADb3+KYLhiP57d8A9J8HWxX16uQi3i99cOIW1PzuDF284gX99aVa7/dMLUli7u7JX1+JVFEU5UroVfWttM3A98DKwHnjCWrvWGPNzrzXfFX8D4oE1SOfxgLV29VG2OTRivJU23/ojlG2GBy+AJb/t88vGRXkYn5NIdlJ0u+0zClJobrWsKj66uQOKoihHQ0jObmvtC8ALAdt+3Mmx8/2e1yBpm/1P1kRILoBZ18D0q+DZr8OS38Cxn4fEYf3enGPzxQ20fOdBZo9I6/frK4qiQDjPyE3OhxtXw3HXQ1S8CD9AeUABtTduhYXf6PPmpMRFMiEnkRfX7MXa3olVK4qiHCnhK/qBpBbJ48Ht7bdveB42vdIvTbhsdj5rdlfxkbp4FEUZIIaO6CfmgSsCKvwSh6wVy79mP7QEn13bm1wwLZf4KA8PvtfzVbkURVGOhqEj+m4PpBS0F/3qvdBYA1io3tfnTYiP8nDRsbn8b/Veymoauj9BURSllxk6og+QUgQVfu6dss2+51V7Oh7fB1w5t5DGllZuW7RJffuKovQ7Q0v0U0eI6DtiW+4v+iX90oRRmfF86fgiHlq6iz+/uqlfrqkoiuIQngujd0ZqkZRmOFwOcelQtkX8/K1N/WbpA/zwnPHUNjRz++tbSIqN5Mvzivrt2oqiDG2GnqUPPr9++WbIHA+R8f0q+i6X4dcXTuasSdn88n/reGVt38cTFEVRYKiLftlmSB8tk7WqdvdrU9wuw58umcqUvGRueGwly3Z0KD6qKIrS6wwt0U/OB4z49Zvq4dAuSHNEv/8sfYeYSDf3fX4GOUnRXHHf+7y6bn+/t0FRlKHF0BJ9TxQkDRdLv2IbYL2Wfm570W9thZdugS2v9XmTMhKiePKrcxmXk8i1Dy7n2ZX9O+JQFGVoMbREHyC1UATfydxJGyWiX71Pau8DrHoUlt4JH9xzdNfa+CIs+insW9PlYWnxUTz6ldnMKkrlpsdX8vzq/h91KIoyNBiCoj8CDqyHD+6V12mjxL1jW2Rmbt1BeNVbS27ne9B6FKWQ370D3v4z3HU8vPLDLg+NjfTwjy/MZHpBCjc8tpLvP/OxLq+oKEqvM/REf/x5kJANO9+BjPFSjC3Ru/pj1R54/ZdQVwFzr4eGSjiwrufXqi2FkSdDzjGw6/1uD4+L8vDAF2dx+ex8nlhWzOzfvMZxv3mNrz20ot0avIqiKD1l6In+qFPgmx/C9/fCtW/INqfUcvFSWPFPmPElmH2tbNv5Xs+vVVsqI4v0sTKKCIH4KA8/P38Sr337U9x4yhhmj0hj8cYDfOZv77B+b/+sKa8oSvgy9ETfISJaArvgE/03fw8YmHeTZPokDZcRQU9oaZIRQ1wGxGdCzQHfTOAQKEiL44ZTR/PnS6fyxLVzaW5t5fw73uEXz69jw74qPtp1UK1/RVGOmKE1I7czYlLAEwP1lTDtCkjKk+0Fx8kyi9aCCbY+fBccLpfHuHSIjIPmOlmv11nG8QiYkpfMc9+Yxx9e3sgD72znH29L/aCYCDffOGUUV88bQaRn6PbfiqKEjioFiKAn5QIGjr/Rt73gOKg90HHhFYfacnjvTsn577CvVB7jMiA+S57XHOhxEzMTorn14mN45aYT+cuCqbw8ezW/zFjErS9t5LN3v6dVOxVFCQm19B1GnwGjWiRv3yH/OHnc+Takj+p4zpqn4eVbYOMLcNmjEJXg2+cv+i2N8rxmf/v37wGjMhMYlZkAH7zMWE8jsZd/m5ueWMmFd77LaROyWLenipzkaOaOSGNafjJF6fG4XUc4SlEUJWxR0Xc489cdt6WPhqR8WP+8b7lFf8q9Bdt2vgsPXghfehlc3sFTbZk8xmVCqzf/vzdr9teWQ2MNZ03OITspmq/8ewUPLd3J2OwElmws5T8fyiSv2Eg3Z03K4aJjc4n0uHC7DFOHJ2OO1F2lKEpYoKLfFcbApAvhvTtEZOMCFjQv3yILsE+5VCz+im2+EUGbpZ8OtlWeH4V7pwOHy6DpMDQ3MC0/haW3nIwFItwuWlstmw/U8PHuSpZtr+D51Xt4+kNf6egTRqfzzVNGs2Z3JSUH6xiXncCsolQK0uJ6r32KMtjZvQIyJ0pSxxBCRb87Jl0E79wG6xfCjC+231e+GfJmQeE8eb1vVXvRd0X4ArfuyJDTNrul8bAIPsjoIaUAj9sXnnG5DGOzExibncDF0/P40bkTWLq1nAiPi60HavjTq5v47F2SihrpcdHYLJ3SCaPTOXtyDtmJ0YzNTmBYckzbe+4+VMdr6/czszCV8TmJvfM5FGWgqN4P950K5/4Fjv38QLemX1HR747syVKUbc3T7UW/qR4OFcMxn4OMcSLw+z6WTgJE9OMyfFk/8Vm9J/pOZhC0iX5XxEd5OHWCBJM/NSaDsyZns3RbOdPzU8lNiWF7WQ0vrdnHQ0t3ccvmj9vOm5SbSE5SDKXVDawqOYS1Uh302hNHcO2JI0mKjeidz6Mo/c3B7TIC74dlUgcbKvrdYQxMvhiW/BY+eli2Tb5YvjRYKePgiYTMcSL6DjWl4tpxiM/sRdEv87vOkX9pc5JiuGBaXtvrUZkJXH9yAl+bP4q9lXXsr2pg2Y4KFq3bT3HFYVJiI/nGyaM5Y2IWD7yzgzuXbOXet7YxZ0QaidERRHlcfHX+SMZkJbCttIbFG0uJjnAxMiOeOSPSumhJmHK4AlY9BnO+duSpvkr/cKhYHusODmw7BgAV/VCYdDG88Tt49jrvButz2zjunOwpsGWR7xzH0neIz5JSzoE01sIzX4VTfhI8QygYtQGWfi/hdhnyUmLJS4llekEKX/3UyA7H/OGzx3DVcYU8t2oPb2wqZW9lPfur6nl+9V5OnZDJK2v309zqm4S2YOZwvjyviDc2lXKguoHk2AiOG5nO1OHJALS2Wlzhll207lmJ8Yw+PfT/qdK/VHp/iyr6SlDSR8F170tRtgcvhM2vQM5U2ZfqFcbsybDyYfEVJmRJ9k76GN97xGdB8Qcd33vXexIvGD4b0q8PrT3+ln713p59pqNgUm4Sk3KTuOXs8QCU1TTwg2c+5qU1+7h0Zj7pSrTEAAAgAElEQVRfP2kkEW4X/3p3B39/YyuPLROrKsrjoqG5FdjI5bPzcRnDE8uLGZ+TyC/On8SozHi2ldXw3492s3RbBZfNyueyWcMpOVjHltIaThiV3i524bD7UB0AuckxWGt5cOlOMhOiOGNidr9kKdU2NHPfW9v53Ox8MhKifEH8uj5cGKepXuaQJOf33TXCmUpvYoOKvtIpGV4BH3UKrFsInmgR8mhvUDN7sjzu/1hcObWlEB9g6R8ul/IMbj9fePEyeazoZAJYMByfflRS3/gkK0vAuCExJ6TD0+OjuOuK6dQ2thAf5ftK/d+Z4zhpXCbr91Yxf0wm+WmxVNY1cftrm3ngne14XC7OnJTNu1vLOfeOt9vO87gMBWmxfP+Zj7nrja0UHzyMtTA+J5ELp+Xy/Md7qTzcyA2njqaxuZWfLlxHVISLx66ZwztbyvnF81Ikb96odC6blc+YrHgq65ooq2lg3uiMdm3sDf6+ZCt3LN7CyuKD3H/VTIyTrtuXgrLsPlj8a/i/rRAR0/3xSnva3DuHBrYdA4CK/pEy+jT46EHY8D+ftQ+QNUke966G4XOk7EI7904mYGUE4C+mJV7R72zWbzBqy0SUM8b0jaX/n2tk3eDLnwj5FGNMUDGdWZjKzMLUttdJMRH86NMTuOq4QqIiXGQmRFNV38Qj7++i1VpykqI5YXQGaXGRPLasmKdXlHDBtFzyU2P54ysb+dUL6xmTFU9clIebHl8FwNwRaWwvq2XBPUupqmvizInZzB2Zxh9f2cjbW8ratScnKZrvnjGWhuZWKmobuWJ2AUmxETS3tFJa00BmQvQRTWbbV1nPfW9vIycpmsUbS3lyRQmXtFn6fSj6B7dDU60s+Zkzpe+uE65Uqk9fCZUR88HlgcYaSPPzeccky1B738ftZ+M6JGTLY80+n+i3tsLu5fLcWbc3FA6XQWyavGfZ5p5+ks4p29w+CN0HDE+NbXueGB0RNH4g7h2f++LsyTnsrayjKD0Oa+HZVbuprm/m8tkF7Civ5dK7lzJxWBJ/uvQYYiM9XDpzOFsO1LD5QDXJsZEY4LcvbuBbT6xqe8+Hlu7kyrkFPL6smJ3lh4l0u0iOjaC+qYW8lFiuO2kk88dmcrihmeqGZmobmomNdJMRH43F8sdXNtLSannsmjl896nV/OK5dZyes5dkkIBuX+HM+SjbpKJ/pFirgVzlCIhOEkt+59uSueNP9hTYt9pvNm6AewfaT9Aq3yJF3pLz5UvYVB/aRJHachHlhBzY/ubRfZ5AHF+xM6FsEBET6WZERjwgSTH+GUgjM+JZ8t35RLgNUR43ANER7rb4g8Pxo9JZtqOCvORYDh5u5MbHV3LrSxuZlJvIjz89gQPVDRw63Eh0hJu3Npdy/SMfdduuLx5fSEFaHH/87DFc+Y/32benhGQXPLRkJfe+vZi/XjaNKXkSuK6sa+LRD3ax91AdY7MTObYgmbFZCW2xh/qmFh54ZwenTchkVGYCa3ZX8pOFa7lgWi6Xzcr3jUKc71jpxqO+r0OOuoMySoqIlec9Kaj4CUZFvyeMPjW46OfPgQ3P+7J4AlM2oX3apuPamXKplHU+uB0yx3d//cPlPku/vlIma0XGdn9eKFTt9l2jtQVc7p6/l7Wwf62UsD7KmkOhEIqvPsLt4riR8n/JT4vlf9+cx/q91Ryb37E0RUur5dV1+9hRfpj4KA8J0R5iIz0cbmymtLoBlzGkxEVw9mQZuQ1PjeXFG06k5dZaaIasiDqamy2fu/d9vnvGWDbsq+L5VXup9o4WDjfKqmw5SdGcOSmb08Zn8duXNrC6pJK739zKrz4zmZ8sXENVXTMrdh7kgXe2My47kYK0WL5Tsx8XULN7Dc++v5O0uEiiPG6q6ptIjo1kZmEKsZHB78fhxma2l9UyNishaGA87HGy6LImym+wsVYWUxoiqOj3hCkLZN3bguPab592JSz5Hbx7u7wOaukHiH5UEow5U0S/YluIol8mgeMEr5uoZp8s1tIbOFkNWBF+p7M6UnYthWeuhYM7ILkAblzd8zaVrIDXfw6f/Ze40XqR2EgP0wtSgu5zuwxnTgotmO0Q4zHQUgnAaYWRTD7tOK74x/v8ZOFa4qM8nDwuk+/krWN4PBTnX8B728p4fcMBHl66iwfe2UFcpJtffmYSf1+yla8/8iEpsRG8cMMJbNxXzb/f28H6fVW8uGYvX4vaRwKwd8tqfrC24xrMEW7DmKwERmXGM70gheNGpvPhroM8vaKED3cdpKnFMqswlT9ecgxbSmv4aNchphekcGx+MocbW6iobeRgbSPx0R4m5ya16xArahvZXlZDRW0Tk3OTyE76hJUxcPz52ZPlN1h3UEVf6YbEHLj4Hx23xyTDzC9L2QaAWD9L3xMF0clSn/+Yy6Rmf8lyyJvuiw2EGsyt9fPpg2TwBBP9+irJF5//fW/p6BBoE328GUg9FP337oCGahh1qox8QnVdBWPTS7BtCbx/F8y/uWfv0V/UH5LUXoC6CrKTonn268eLZZ2dQITbBffcBI2HyZ92Oflp+Vw6M5+DtY28vuEA0/KTGZERz8njMvndSxv40vFFjMqMZ1RmPOdMkQ7ovU27SXiklkbrpsjsY9ENx9FgpZxGQnQEew7V8e7WctbtreKD7RU8u3JPW/NGZsTx5XkjSI+P5M+vbuKEWxcTRSOfdi3lntbZ1BPV4SONyIjj9AnZFKXHsmLnQf770R4aW8T9F+E2XDgtj/y0WKrqmqiqb+JgbRN7q+qpqmtibFYCk/OSSIuLJDEmgsToCFqtZXtZLTERbs49ZhjNra08vqyY6Ag3n52RR3OL5eW1+xieGssMb4e8t7KexJiIdqO51lZLXVMLcQHbNu6vprq+mVGZ8aTGRXb8Hzn+/GyJhbQePsjamkRW7Kyg1UJdUwsf7jxIQ3Mrt148pV05kuaW1rYaV59UVPR7mznXwdK/S0pnoMjNuxFe/xX8Zar4ExsqYdz3ZBGXmFRf2mZgWqc/Lc0iLLHpEO+IficZPB89CB89BHkzg1cJDUag6PcEa8XSH3WapLhuWQSHdkLG2J69X9kmeXzvTpj91V639ntM3SG4+wQ4/04oOkG21fplC3mDhHFRHl9cwVoo7xi0T4mL5KLpvhjFsOQY/rJgWtDLzvUOGmvTjyGl/ENGeUp9KcXAqMx4ThzjG2VuL6vl3a1ljM5MYGZhSpvVfur4LB79YBefbl3M5OV38bOkxTw39je0po0iNTaSlLhIdpUf5qkVJdz31jaaWy3RES4WzBrOSeMySYjy8OzKPTy+vJjG5lYiPS6SYiJIjokgOymaYUnRrN1TxUtrO08r/s2L67HAocNNAPxt8RZqG5qpqpfKtCPS46iqb25bLyIlNoLhqbEkRkewZk8lVXVNnDA6g1lFqXy06xDLdlRQWdfU9v6RHhexkW5iIuQvNS6Sr9Uv53ii+NpzFTxg4Oq7X+X1hnHt2jUiPY7S6gauuO99/v3lWazbU8VLa/axaL2M1C+dOZwvHFdIXkosNQ3N3PH6FkZnxnPBtFzW7Knk70u2MjY7gc/OGE5u8uBKqVXR720SsuD4b4ovO5B5N8ns3g/uhuYGWY5x2hWyL22kWPobXoCnvghffTu4H9yZ8BOX7mfpe11G1sLTX4YJn4Fxn4YP7pXtB3eG3n5n6AtSSsKfdc9Kqej8OV0Hviq2SYeRPxtSCr1t2HEUor9ZJsFVbB1c1v7eVeIf3rbET/S99yw+K3hmyOEK6exBfMmRcbB5kXTkY85ovyZDZ9RKMkDKhJPhrQ+hbGM70Q+kKD2OovSOFVQL0+Nkgt3i/wKG+KYyLlv9Rbjx47YZ53NGpHHJzOE0t7RScrCOlNjIdjWXZhSm8v2zx2MMRLtagxortQ3NVHpHAVV1zVhrKUqPY0f5YR54ZzvWwnUnjaS6vpm73thKUkwEV86RjKyFq/YwNSGaafnJ1DS0UHzwMCUH6zhY28gZE7JJjo1goXd2eGFaLGdMzGJ2URpp8ZFsOVBDaU0D9Y0tHG5s4XBTCxU1jUTU7qHMk8WMUSNhA5xWFMk5k4/h+FHpREe4cLkMidERLN9RwZX/+IB5v1sMQGK0h9MmZFPf3ML97+zggXd28JlpuXywvYJdFVIA8a43trK1tIb4KA8vrd3HbYs2kx4fxciMOD49JYfzjsklJtLNlgM1PPT+TtbuqSI7MYpx2YlcMC2XwiD/p94mJNE3xpwJ/AVwA/dZa3/byXEXA08CM621y73bpgB3A4lAq3dfkKWmwoiTf9j5vuThcPovO25PHSni8eqPoLke1vwH5n+v43GOJRmbJiMEd5TP0j+0UwrDbXwJTvq+tz4QHcs/1ByQhV+CWf+VJeIqcoTb/5wnvNUIMyfIojGOoAeya6k85s/1ubgO7gh+bHe0tkiW0+xroGK7WPvzbvKtbxwKjd5Mjd7O0DiwXh7L/DJonNnSaaPb12Jy8J+EV71POvv/fEU6c080nHUrTP9C19d1vgMFx8FbSAbP+HN7/DGoLBED4qL74J/nyPdwwvntDvG4XZ0KUkykW9r0l2Pgon/A2DPb7Y+L8hAX5WEY7S3ezMRoZhWlttt2/CifS3T2iDQundn9jOP/O3McVXVNpAS4cuaP7cQ1eXcdxI3h62fNhA1w2eQE8BtlOcwoTOWfX5zJi2v2cdK4TOaOSGtblnTPoTrufmMrj35QTGZiFE9cO5ed5bXctmgzl8wYzi1nj6eqrokXPt7LttJaVpUc4kfPruVHz/qMwSiPi2n5yWwrreXVdfv5y2ubOXV8Jvd9YWa3n/lo6Fb0jTFu4G/AaUAJsMwYs9Bauy7guATgm8D7fts8wEPAldbaVcaYNKAJpSNpI2H1Y1CDiPn650T0W1vFp/3u7dIZnPITOT42TUQsIRuqvD7bPSvlsbUJXvmBuH/SRkpn4M9bf4L3/y7+9qSAL3tliUw0O7SrzaIExKoFmPN1OXflI9KxBKN4qViK6WOljRFxPRf9Q7ugpUFKWuQfJ9lRJct85ay743AF3DYZzvurrI3Qm5R6Rb90k2+b01Gmj5YMr5ZmcPv9zPzjNtV7JdhfVwFTL5fP+sJ3IPdY3wzvYDhpv6lFkJh39GmblcUy6hw+RxILNr/aQfS7Zc9HMndl13sdRL+vcbtMB8HvkspiGDbN5ybsIld/9og0ZgcpGjgsOYafnT+J7545jki3i0iPi1lFqXx2xvC2Y5KiXFwbuwTOvxzriWLN7ire3Czfj6SYCD49JYfkWGn3vsp6/vNRCa1+dav6ilCiEbOALdbabdbaRuAxINg34hfArYC/FX86sNpauwrAWlturRPlUtrhBGLzZsEJ35FyDhXb4LWfwWOXQekG+WF9/KQc56SDZk2SxSAA9q6UEs8X3y+vZ31F3tff0rdWOhTo6PaxVkQ/OV/EyN/S3+vtUObfLAtPOOmmwdi1VATE5RLRTykUK70nOP789LFQeLzMRN62JPTziz8QMdrTSb69tdBU17O2OZZ+xVaJw4CvGJ6Tzltf2f4cf0u/aq/PnTbqVG92Uio8fbWk4XZG2+S/THHrlPWG6OdJ5zRyPmx5Te7LkbDfm0E02OcNNNZKVlrycBn9uSOPaoJWfJSnzfrvwI634X/fgg3PY4xhcl4SXz9pFF8/aRRXzCloE3yA7KRorps/iutP7vvU5lBEPxfwc/RS4t3WhjFmGjDcWvt8wLljAGuMedkY86Ex5v+CXcAYc40xZrkxZnlpaQ+Dh5908maIZX76L31D9cW/gXf/KjX7b1onQrz6cdnnuE0K54kbp7JELP3M8XL+Dath3rek1n7Nfp+w7fkQqrzB2sARwOFyKR+RlOcVfb+g5N5V4oKKTpS2lqyQUUggteUi1PlzfNtSCntu6beJ/mgZPeRO94n+ppdhxT+7Pt+Z8Xywk05nzdPwh7Edxbk7rIUDGyQjq7XZ16nVlso2J+spUFAqtskoDcTSdzrk5HxZme2Cv0sH/9GDnV+7thQ8MRIPyJwgQtvceGTtd2hthcrdvhHfqFOheg8cWNf1eYE4MazSDT1rx9Gw6jFxLbU0d3+sk6iQNFwMkpiUntXfqd7X/Up4TgfY2T0pWQHv/OXIr32UhCL6wRyhbWaAMcYF/Bn4dpDjPMA84HLv4wXGmFM6vJm191hrZ1hrZ2RkZATuHhqkFMJ3NnqDnwWSTvbxE2LRn/kbmXw19XLferuxXl9o4fHyuOMdscaHeesBpRSIpZ3sXWDFSVNbt1DKSGA6+vodq9MR/ZoA907OMfI8b6YEI8s20YFtEvRqJ/qpRSL6/tajtaFZk2WbpINzPu+I+TKyqdoL/70OXvp+1z92ZxTUWadT/IF8ln0dc927pGqPN/vqHG87vT/ww2Vy72K8uf+BlTbLt8p9jIgT4XD+L061zJEnSxB4r69UBM0N7d/DKeZnjFRnba7vfCRTX9l1h3C4TNxnSV63xKhT5dG/THgoOKJ/aGfPR049ZfeH8v/trGP3x7nfzueNSTlyS99a+Pf58OzXuz7O+U50Nvp547fw6o/b/6/7gVBEvwQY7vc6D9jj9zoBmAQsMcbsAOYAC40xM7znvmGtLbPWHgZeAI7tjYaHPRPOk8ezfufzPToBvugkX5ZE1iR5veoR+fL6F4EDP9Hf6XXtLITCE2RiV6B7p80KyvNWCvVa+ocrpIPwF31o7+JpPAz/+7YEJRNzYZjfvzmlUEYQTidStQfunCsL03RH2eb2JapHzJcSEU9f7V0nuLbjj2bN0xJEbW31iX7FjuCdjONuCZZtFYwtr8m9cPz5ju/b+WHXlklH3Sb6foJirVj6qSNlrkf1Hvm/eKLbT+TLnOBzl+xfB7/OhWV+80JqDviOdyYI7nq3Y1uthb/PgyW/6fzzOB19svcnnjhM3HebX+36PvjT3CCdc0qh/G/Kt4R+bmfseh9e/B7ce4oYNF3hTHgMZZTh1NFPPgrRL/5ArnWgm+uVdiH6DdW+Eev79xzZ9Y+SUER/GTDaGFNkjIkEFgALnZ3W2kprbbq1ttBaWwgsBc7zZu+8DEwxxsR6g7qfAo5w3DhEmXMdXPG0pF86pI6AkadAkl9Gg8sNBcf7vkDDAkXfe+yhnTJkr9gmHUpKgc+9U1suItkm+vkiXLUHRDj2eWfTOqKfNko6Gn/Rf+c2Kfc782pJN/Wfo9CWtrldOpAHLxTR/OjB9kJcWy5zGHb6CVjZpvbpiHkzxRe7821fB7fTTxQaD8N/roWF3xRBr68UEWusDl4AzRGo/d5Mm8W/gftO63gciAX76AJ5f8efnztDOjln1NOV6NeWQUOVBNcTcryW/i75H/lnFmVNFKFobYHtb0hg/n/fho+f8ruG130Uly7xjp1BRL98q4jc/i5GMYf8RncOI0+SuEyoLqOyTTICdZYK7cqv/9FD8v/vit0r4IGzxHW35yMxVPwp3woPXuBzyzjGRHciDPJ5XR7fbPaeuHdWelfQqyzuOArzx/lOVGzteC+3LIKWRgnYf/xk+4WR+phuRd9a2wxcjwj4euAJa+1aY8zPjTHndXPuQeBPSMexEvjQWvu/o2/2ECAyTobagWmGF90Hn3us/bYCr4vH5RGB8yc+S9I6D+70BnANjD3HW+TNa/W88kO4a57EDzwx4kqJyxC3QWONz5J2RN/lErEr8frLmxtg+f1STuLs3/tcMQ6O6Fdsg8evlMepV0idH39B2r5EOoaPHpLXteUSZ/C39D2Rvs971q1iNfsL3u7lIpJ7PoSld8q2yV4xChz+Nzf67oFj6a99Bko+CB5I3btKfqi73oXlD4jwxqVJ+9os/VJxRwUTfWdUkTrSl3XliL4/WRPl3ldsk441YZh85meuhbIt0hn713UqmCsi3RqQI1HsTaQLtmKbg//oziF7stzDUNwl4Lt3Ez4DxtW56Fsr37Gtr3WcA+LQVA/PfE3uz7c3SCZToOtt/ULY+rrPGHGWDA3J0i+W0YxTU+pILf3Gw5JOHZ0M2M7vbd0hGYFkTfbGfAIm5G14QWI7n/m7uNc+/FfobThKQppLbK19wVo7xlo70lr7K++2H1trFwY5dr6To+99/ZC1dqK1dpK1NmggVzkCYlM7plk66YuZ4zvOAna5ZCh7aJf48/PnygSy5AIR3ZYmSbOLyxQRSimQjsaxJGtLJUCcnN9ezIfPkpFDQ7UIZW0pzL42eJuT8wEDb9wqFvqn/wSn/Fj2bXrJd9zO9+Rx44vip28L4gZMPDrh23DqTyX+UXCciLATVN75rlwrKkk6osh46Yygo1//4A5xR8Rlihulao/PDxtsURtnZBOfJfudOkkZY8UN1dIsPvy4DO/kJtN+dOH88NtZ+js7in7mBHncv0YW2cmfLZ19a4uk9daWtS+PUXC8jCACXVQl3pXaDu3qPH5SWQKRCV4R8+Lc71AzcfavEcMicwKkFHUuvvvX+vY57rFAFv9K/gfn3S6CnDVJRqH+7XdcdpXe4oCOpR9KeytL2o+Uo5OPTPTXPyejxuO+Ia87y0pzvrtOUob/PWlpkiSEMWdKB1t4ghgo3QWGe4lPbgEJxUf2ZLEuh88Ovj+5QMTwwFrflzClQARv72qx6OZ+Ha5ZDBfcLfsdn3FNqQSIHSvfIW8GYOHl70udnfSxMOKk4Nf3RIkL5OB2Gb1MvVw6ntzpMpHMYdd73nK3FWKlrnxYxGRYQDmCgrkyQQukw6uvlM8G4urJngQzrpLXw6aJEEHHH6jj2hl/rsQcPvy3b1+wIHXxB3IvnQ7LEf30MRJb2L/G24mkiyUZndReUMq3Ssppcr6IfkuD7A8U/YyxYjFvXSzumbyZEgMonCdttC3tYwD5c72fPcDF4yzP2XS4fSYW+DoCJ13Tf0TpzAQPdg+CsX8tZI6TlM+Mce3PK90ET39FvkcfP0lbXsiBIKK/630ZCUy/yhdQzp4sAXP/meIlXtGvKoGGGhmNuiO9bqZuMsIPFfv8+SAdS1Nt6K6sD/8t3wFnJn1n62A4HdD4TwOm/T3Z8Xb7JICzfifG09NXd9/+XkBFPxxwuUWwnYlbgSTn+yZaOaLvCM3a/8hj3gwRSCcm4Cz1uPF/8sUu+lT79yyaDzO+DB89LJbY7Gu6nvGaWiQW5adv8x035kyx2moOyHB4/1qJCbgjxfJZ9SjM+GLXC7o4gcyd78oPt3iZWL4zvyLurvw5kvkUn93R0ndE3wmaL7tP2ogRN0ogJctFgKcskM8+5RLZ7oj/4l/Jo9PeQNdByQcSD3FH+EpogC+TxCEiRlxAa56W107gfPLFvqClv+gnDxfr1T+2UV8pwuoE1B03RGOtZJ3cNlliKo7o+xOV0D5OsXcVbHuj4/1w2L/Wt3Jcxhi5r868hbf/LFloT1wpbpFRp4p1HSj6jYfhv1+Te+E/Y92ZpOa4eKr2SAAcxNJ37sfw2dKJdpUa3NIk5/rfbydJoj7Ar19ZAkvvaj/C2L9WRqozviSjvYi4zl1gZRvFYMkYJ781f0t/6+syn8YxkrImilt0+xvw5h86b38voaIfLiTn+9brDSTFm8Ez7FifleNk9az5j1iVgda0Iyrv3y0W6zGXtd/v9oib5rql8iOdekXX7Tv79/CFZ9tbWWPOAKy4eIo/kOejT5MMnQ3Pi1V8/I3df+6k4fIee1eKxV5wnFznq2/7zk8t6vgDrdgqftXhc+RataVQdKKcG2jlVu4WwRg+y/fZc6fLvuFzYNa1sPkVee3MoYhN9Yn+oWLY/pZvVnDiML/PUNDxc2VNEAvWFdFWDZLx53nTbelY/XTUKeIWc1I3S5bL/Zx8sff6OyX28o8zpKOOy5By3od2dRR9aB+neP5b8PDFnZSV2C7C64wEM8b5fNgNNVKvKWO8jOIqd0l7Msf7RP+1X8Dt0+Dek+T/8Zm/ta8/lDkBML5rO64dd6R0AI5LpOhEeezKr1+1R0Zi/p83WOwF4LWfw0vfaz+D+oN7JNPq2M+L4ZJa1Ll7p3STjJhcbhm5+bueSpaJceW/Bsa0K2Vdjd2dzH/pRVT0hwKOVe9fnyUxV4Sueo8EfyMD6qo4wtVcL5ZNZ/XGM8aIf7O7ssmZ430i6ZA9RQRh8a9lROGKkADx2LNl//SrQlucfcYXxXpy8qbzj/Nd02l3sAli5VvF8o6I9rk0ik6Qujnl3mUoX/kRPHejlJYAr1srAJcLzr4VrvyvdI7O54xJ8eXpr34MsHDMAnntb+kHunfAZznnTPHd29hUyd6C9pY+iMspPhOe+pK4Coo/kM584gWy/9Auic3s/xjOvQ3Ou0O21R1s3xE7OHGKuoMSFG9pFDdNYA7+ppflcbQ348npoFY9Kh13U610kCf9QNxsY8/2iX5zoxQFNC75/s3/vk+8HaLiJWttv5/ouyJkNFflZ+k75wVzGzkEpqdCcNGvKZU4FcC+Vb79q5+AyZ/1xbZSCjsaEk11ItplG32xEedetrbIZ979ocy898cYOPd2uOwx+T71IVplcyhQcLxM+nEEB8RaTcqVH37e9I7neCJlGN5YK1ZsX2AMXHAX3HeqpOflzRTrZ9JFYrGd+N3Q3mfet0TAVz4sP7T4jI7HpBTJzE3/uv7lW+S+gIhs6QYRj4M74MOlYqm+f7e4DdYvFCsvq4uaOCNPkj+HmBS5hrWw8lH5PziZTE5ZbE908DULnGBuoDjMukbeM7CjiE31FUy7a5783zInyIgiJkUsfWeUMOYsuaazvGegewl8cYrVT4p1PO9b8PafpIM+/Re+4za9KMc6ZUSyJoi/++3bxBJOKZSYQ8Fx8v80RtrVcD+seUp82xfcBePO7vy+Zk+S2BPICCZ7klxvz0d+dYhGdl6HqGyLjILb0lP97p0j+of9UiY/+rd0csYtrq1JF0mtqabDcv8dUotkdOesMFe9H/42Uzqlw+W+0XHGOPkOVWwXt1tLg4wYA4hVxbQAAA1ISURBVOnpehNHiFr6Q4GEbLjymfYuBfC5FRyfcSDDpokVHYq13VOGTYVTfiTPnYBkdKIEt0JdnN0YOPcvXl/7l4Ifk1JIuxS7hhopg+CI1YTzYfTpMvJIHy2Ct/IR+YEWfUp+xDlTpTMMFcenv/NdcV34u8gioqXOjlMOIJDc6dIhjAqYwD76VPjmhx1HZiDCesHdIjIphRIfAV967u7lIngJWXLN+bfI/oxxHd/LsVI/uEeC6/Nvlnu08hGf+6G+SiZOOdlRDmf+1ltvaZt8ZufzOY/O9d76k6QIj5jf8fr+ZE0Wi7rukIxWcqeLwVJ/SLYbt3R6meM6unfKt8I9n5K5Ac7/3t+9kzoCcR95YwatLZKOW3Ri+85m/XPiwvJfhD51hHQOTsHDt/4o36sR8+U75IzKnLjTumd8abSdJV30A2rpD2WSC4C3xKUSjCuf6Z92zP2GWKFHUx7YHQEX3t35/lRvBs+ej8Ql1ZY+6S2MNuE8X0A3zevqee8OiWdc/hQs+XXHuEd3xKSKZffPsyEqsWPlypQC3zKagSTmwPd2Hrn1N+USX4DZIblAxLCprn0HP+5s+PYm6QQCcdY+KN8swVdPFIw7V3z0ez4UN9fW1yWff+xZ7c+NSpASyy9825fl4o8T+C7fLHNGulvf2QnmPvxZSZfMnSEuIRBXSVyGWNrDpklHUr5V0mJbmiQjpqlO3EN1FZKe639PY5JllOfMaN6ySNxAZ/watrwK65+X/2HxB7IIkj9OVpjj4lnxgHze825vf1zqCDEcVvwbcqdJx9uXhlQ3qKU/lCk6UYK7gXnwDsb0fg36YLhckjIazLfdW+RMFbfCKz8Ul4BT0CzYwi7O/Ti0U1b/8kTKvIAjLTc8+jQZPZz8Q/jK4o6B9ov+Aef8sfPze2u4n5zvnZ1b3DEmEUzwwTvXwJvZMmK+PI46RcTWmVux6SU5JtAFBeIyvGZJ8CBxXLpvHkhXbp2295opKa71lTKaG3OGb/nPvat8n2HmVyTA+8bv5PWin0oHddF98j+t2h08fpE/R7K+WpplLouzbnXOMdJRrHxU0mQdV6CDY0iUb/VlbnXmkpx+lQSy1z8Pw/u2Xn53qKU/lDnmUvkbCngi5cd/z0ni867ZL7GKYK6NhGyZ1NVY09GKPRLyZsDlT3a+31kbua9JLvCt29uZKy8QY6RDLH7fJ/qxqZKptOkluXcbX4DRZ7RfLyBUMsfDjrKOrqFgxKXJ7Fx/Er2i31znGy0lZMGsq+HdO+T/t/wf4uKadCFgJcgdLH5RMBeW3SujwE0vwZjT5fvilPl496/yfoGdW2Ke+O9fulkSHuZeH7xTAVnJLjZd6kUNoGsH1NJXhhJZEyW9tGa//EDP+l3wkYwx3nQ7j2+S0CcZZwTln/4ZCsOmibD5l/YYc4akTz75BXGbBLo8QmX6VTKzOtS4TSD+8Sn/QPjxN0oMYvk/YPIlUqoDYMIFMlIbG2Rk4WR7vfsXEWXnmKyJEi+oKpFZs4HxHLdH4gsJOXDhvXDazztvrycSpn5OngcL4vYjaukrQ4vZ14hwBRY5C2TyJSIGg2UR9qPBmaeRPfnIXEan/ETcFf4phGPOgEU/kYlgp/9KhLEnTLoQOIqVzDxRvoV+/OMicemSPrt/nYiwU2PH5YJL/h38vRJzJPC8/jnpGJ2OPiJGRjsH1nV07Th88QXAhJZmecK3xZgIrITbz6joK0OPlCCToQKZe13ft6O/cFwaweYYdEVkbMcga8Y4KbmRkC2VYAeSxFyv6Ge33x4seNwd+cdJqm7Rie1jL9lTuhZ9p1MJhZhkmdg1wKjoK0q4ExUvSzGG6s/vCmPg6kViaffxJKJuScqTWdjB5jkcKQVzZU2KwMDy9C/IrO3+ir/0Ayr6ijIUmPiZ7o8Jlc7KffQ3TjC3s7TXI2H8uZKTP+ni9tsLjvPl2YcJKvqKonwycdI2e8PSj0mBc/q+2NlgQEVfUZRPJhMvlFnBziQpJSRU9BVF+WSSPNxXwkMJGc3TVxRFGUKo6CuKogwhVPQVRVGGECr6iqIoQwgVfUVRlCGEir6iKMoQQkVfURRlCKGiryiKMoQw1tqBbkM7jDGlwM6jeIt0oKyXmtObaLuOnMHaNm3XkTFY2wWDt209aVeBtTaju4MGnegfLcaY5dbaI6wh2/dou46cwdo2bdeRMVjbBYO3bX3ZLnXvKIqiDCFU9BVFUYYQ4Sj69wx0AzpB23XkDNa2abuOjMHaLhi8beuzdoWdT19RFEXpnHC09BVFUZROUNFXFEUZQoSN6BtjzjTGbDTGbDHG3DyA7RhujFlsjFlvjFlrjLnBuz3VGPOqMWaz9zFlgNrnNsZ8ZIx53vu6yBjzvrddjxtjIgeoXcnGmKeMMRu8927uYLhnxpibvP/HNcaYR40x0QN1z4wx9xtjDhhj1vhtC3qPjHC79/ew2hhzbD+36/fe/+VqY8wzxphkv323eNu10RhzRn+2y2/fd4wx1hiT7n3db/erq7YZY77hvS9rjTG3+m3vvXtmrf3E/wFuYCswAogEVgETBqgtOcCx3ucJwCZgAnArcLN3+83A7waofd8CHgGe975+AljgfX4X8LUBate/gKu9zyOB5IG+Z0AusB2I8btXVw3UPQNOBI4F1vhtC3qPgLOBFwEDzAHe7+d2nQ54vM9/59euCd7fZxRQ5P3duvurXd7tw4GXkUmg6f19v7q4ZycBi4Ao7+vMvrhnff5F7Y8/YC7wst/rW4BbBrpd3rY8C5wGbARyvNtygI0D0JY84DXgZOB57xe8zO/H2e4+9mO7Er3iagK2D+g984p+MZCKLC36PHDGQN4zoDBAKILeI+Bu4LJgx/VHuwL2XQA87H3e7rfpFd+5/dku4CngGGCHn+j36/3q5H/5BHBqkON69Z6Fi3vH+XE6lHi3DSjGmEJgGvA+kGWt3QvgfcwcgCbdBvwf0Op9nQYcstY2e18P1H0bAZQCD3hdT/cZY+IY4Htmrd0N/AHYBewFKoEVDI575tDZPRpMv4kvIVY0DHC7jDHnAbuttasCdg2G+zUGOMHrOnzDGDOzL9oWLqJvgmwb0FxUY0w88DRwo7W2aiDb4m3Pp4ED1toV/puDHDoQ982DDHX/bq2dBtQirooBxesfPx8ZUg8D4oCzghw6GPOeB8X/1hjzA6AZeNjZFOSwfmmXMeb/27ub0DjqMI7j35/EFkvFKlosBqytVUTQ+HIoVqFYDypSPFRajDWIRy9epJT4gt4VL4X24KHa4EsllhzFKIEeNNWQWqlvRQsuUl9ACkWUUh8P/2fMGppUJJlZMr8PLNn9z+zw7JP9Pzv7n9n/rACGgefPt/g8bXXnqw+4nDK89AzwjiSxwLEtlaLfoYzTVfqBHxuKBUkXUwr+SESMZvNPktbk8jXAzzWHtQnYKukk8BZliOdVYJWkvlynqbx1gE5EfJKP36V8CDSds/uA7yPil4g4C4wCd9EbOavMlaPG+4SkIeAhYDByXKLhuNZTPsCPZj/oB6YkXd1wXJUOMBrFJOUb+ZULHdtSKfpHgA15VsUyYAcw1kQg+cn8GvBlRLzStWgMGMr7Q5Sx/tpExO6I6I+ItZT8fBgRg8BHwLam4srYTgE/SLoxm7YAx2k4Z5RhnY2SVuT/tYqr8Zx1mStHY8DjeVbKRuB0NQxUB0n3A7uArRHx+6x4d0haLuk6YAMwWUdMEXEsIlZHxNrsBx3KSRenaDhf6RBlZwxJN1BOaPiVhc7ZYh6oqPNGOfr+DeXI9nCDcdxN+er1OTCdtwcp4+fjwLf594oGY9zMzNk76/INdAI4SJ450EBMA8CnmbdDlK+5jecMeBH4CvgCeINyBkUjOQPepBxbOEspWE/OlSPKkMCe7A/HgDtrjusEZRy66gN7u9Yfzri+Bh6oM65Zy08ycyC3tnzNk7NlwIF8r00B9y5GzjwNg5lZiyyV4R0zM/sPXPTNzFrERd/MrEVc9M3MWsRF38ysRVz0zRaQpM3KGUzNepGLvplZi7joWytJekzSpKRpSftUrjNwRtLLkqYkjUu6KtcdkPRx19zw1Zz110v6QNLRfM763PxKzVwbYCR/zWvWE1z0rXUk3QRsBzZFxABwDhikTKg2FRG3AxPAC/mU14FdEXEL5deaVfsIsCcibqXMyVP9bP824GnKPOjrKPMemfWEvguvYrbkbAHuAI7kTvgllInK/gLeznUOAKOSLgNWRcREtu8HDkq6FLgmIt4DiIg/AHJ7kxHRycfTlHnTDy/+yzK7MBd9ayMB+yNi978apedmrTffHCXzDdn82XX/HO5n1kM8vGNtNA5sk7Qa/rnO7LWU/lDNnvkocDgiTgO/Sbon23cCE1GukdCR9HBuY3nO127W07wHYq0TEcclPQu8L+kiykyHT1Eu3nKzpM8oV8nank8ZAvZmUf8OeCLbdwL7JL2U23ikxpdh9r94lk2zJOlMRKxsOg6zxeThHTOzFvGevplZi3hP38ysRVz0zcxaxEXfzKxFXPTNzFrERd/MrEX+BsFWud9ewo6WAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "import numpy as np \n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from keras.models import Sequential\n",
    "from keras.layers.core import Dense, Dropout, Activation, Flatten\n",
    "from keras.layers.convolutional import Conv1D\n",
    "from keras.layers import AveragePooling1D\n",
    "from keras.utils import np_utils\n",
    "from keras.optimizers import Adam\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from contextlib import redirect_stdout\n",
    "from keras.callbacks import EarlyStopping, ModelCheckpoint\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from keras.wrappers.scikit_learn import KerasClassifier\n",
    "\n",
    "def get_data():\n",
    "    data = pd.read_csv(\"/kaggle/input/train-cnn/p2pData.txt\", sep=\" \", header=None)\n",
    "    label = pd.read_csv(\"/kaggle/input/train-cnn/p2pLabel.txt\", sep=\" \", header=None)\n",
    "    data_columns = data.shape[1]\n",
    "\n",
    "    for i in range(0, data_columns):   ## These three lines make sure there're no NaN columns\n",
    "        if data[i][0] != data[i][0]:\n",
    "            del data[i]\n",
    "\n",
    "    data_columns = data.shape[1]\n",
    "    data.columns = np.arange(0, data_columns)\n",
    "\n",
    "    return data, label\n",
    "\n",
    "\n",
    "data, label = get_data()\n",
    "#data = data[:5000]\n",
    "#label = label[:5000]\n",
    "selectedFeatureList=[0, 1, 61, 62]\n",
    "data=data[selectedFeatureList]\n",
    "data_columns = data.shape[1]\n",
    "data.columns = np.arange(0, data_columns)\n",
    "\n",
    "scaler=StandardScaler()\n",
    "data=scaler.fit_transform(data)\n",
    "\n",
    "data=np.array(data)\n",
    "label=np.array(label)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(data, label, test_size=0.2)\n",
    "n_samples, n_features = X_train.shape\n",
    "\n",
    "\n",
    "def reshapeData(data):\n",
    "    n_samples, n_features = data.shape\n",
    "    data = np.reshape(data, (n_samples, n_features, 1))\n",
    "    return data\n",
    "\n",
    "\n",
    "X_train = reshapeData(X_train)\n",
    "X_test = reshapeData(X_test)\n",
    "\n",
    "\n",
    "def toOneHot(label):\n",
    "    enc = OneHotEncoder()\n",
    "    enc.fit(label)\n",
    "    label = enc.transform(label).toarray()\n",
    "    return label\n",
    "\n",
    "\n",
    "y_train = toOneHot(y_train)\n",
    "y_test = toOneHot(y_test)\n",
    "\n",
    "\n",
    "def create_model():\n",
    "\n",
    "    adam = Adam(lr=0.004,\n",
    "                beta_1=0.9,\n",
    "                beta_2=0.999,\n",
    "                epsilon=1e-08,\n",
    "                amsgrad=True)\n",
    "\n",
    "    model = Sequential()\n",
    "\n",
    "    model.add(\n",
    "        Conv1D(64,\n",
    "               kernel_size=7,\n",
    "               strides=2,\n",
    "               input_shape=(n_features, 1),\n",
    "               kernel_initializer=\"normal\",\n",
    "               padding='same'))\n",
    "    model.add(Activation(\"relu\"))\n",
    "    model.add(Conv1D(64, kernel_size=7, strides=2, padding='same'))\n",
    "    model.add(Activation(\"relu\"))\n",
    "    model.add(AveragePooling1D(pool_size=1))\n",
    "    model.add(Dropout(0.2))\n",
    "\n",
    "    model.add(Conv1D(128, kernel_size=7, strides=2, padding='same'))\n",
    "    model.add(Activation(\"relu\"))\n",
    "    model.add(Conv1D(128, kernel_size=7, strides=2, padding='same'))\n",
    "    model.add(Activation(\"relu\"))\n",
    "    model.add(AveragePooling1D(pool_size=1))\n",
    "    model.add(Dropout(0.2))\n",
    "\n",
    "    model.add(Flatten())\n",
    "    model.add(Dense(512, kernel_initializer=\"normal\"))\n",
    "    model.add(Dense(512, kernel_initializer=\"normal\"))\n",
    "    model.add(Dense(5))\n",
    "    model.add(Activation('softmax'))\n",
    "    model.compile(loss='categorical_crossentropy',\n",
    "                  optimizer=adam,\n",
    "                  metrics=['accuracy'])\n",
    "    return model\n",
    "\n",
    "\n",
    "model = create_model()\n",
    "\n",
    "with open('Model_Architecture.txt', 'w') as f:\n",
    "    with redirect_stdout(f):\n",
    "        model.summary()\n",
    "\n",
    "callbacks = [\n",
    "    EarlyStopping(monitor='val_acc',\n",
    "                  min_delta=0.00001,\n",
    "                  patience=6,\n",
    "                  verbose=0,\n",
    "                  mode='max',\n",
    "                  restore_best_weights=False),\n",
    "    ModelCheckpoint(filepath='./best_model.h5',\n",
    "                    monitor='val_acc',\n",
    "                    mode=\"max\",\n",
    "                    save_weights_only=False,\n",
    "                    save_best_only=True)\n",
    "]\n",
    "\n",
    "history = model.fit(X_train,\n",
    "                    y_train,\n",
    "                    batch_size=120,\n",
    "                    epochs=160,\n",
    "                    verbose=0,\n",
    "                    validation_split=0.2,\n",
    "                    callbacks=callbacks)\n",
    "\n",
    "#model.save(\"./best_model.h5\")\n",
    "\n",
    "#Evaluating the model on the test data\n",
    "score, accuracy = model.evaluate(X_test, y_test, verbose=0)\n",
    "print('Test score:', score)\n",
    "print('Test accuracy:', accuracy)\n",
    "\n",
    "plt.plot(history.history['loss'], label='train')\n",
    "plt.plot(history.history['val_loss'], label='val')\n",
    "plt.xlabel(\"epoch\")\n",
    "plt.title(\"Loss\")\n",
    "plt.legend(loc=\"best\")\n",
    "plt.savefig(\"./loss.png\")\n",
    "plt.show()\n",
    "\n",
    "plt.plot(history.history['accuracy'], label='train')\n",
    "plt.plot(history.history['val_accuracy'], label='val')\n",
    "plt.xlabel(\"epoch\")\n",
    "plt.title(\"Accuracy\")\n",
    "plt.legend(loc=\"best\")\n",
    "plt.savefig(\"./accuracy.png\")\n",
    "plt.show()"
   ]
  }
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